Understanding the Impacts of Weather and Climate

Change on Travel Behaviour

Chengxi Liu

Doctoral Thesis

Division of Transport Planning, Economics and Engineering

Department of Transport Science

School of Architecture and the Built Environment

KTH Royal Institute of Technology

Stockholm, 2016

Understanding the impacts of

weather and climate change on travel behaviour

TRITA-TSC-PHD 16-005

ISBN 978-91-87353-89-5

KTH Royal Institute of Technology

School of Architecture and the Built Environment

Department of Transport Science

SE-100 44 Stockholm

SWEDEN

Akademisk avhandling som med tillstånd av Kungliga Tekniska Högskolan framlägges till

offentlig granskning för avläggande av teknologie doktorsexamen i transportvetenskap

fredagen den 10 juni 2016 klockan 13.30 i L1 Drottning Kristinas väg 30,

Kungliga Tekniska högskolan.

○c Chengxi Liu, June, 2016. Tryck: Universitetsservice US AB

ii

Abstract

Human behaviour produces massive greenhouse gas emissions, which trigger climate

change and more unpredictable weather conditions. The fluctuation of daily weather corresponds

to variations of everyday travel behaviour. This influence, although is less noticeable, can have a

strong impact on the transport system. Specifically, the climate in Sweden is becoming

warmer in the recent 10 years. However, it is largely unknown to what extent the change of

travel behaviour would respond to the changing weather. Understanding these issues would help

analysts and policy makers incorporate local weather and climate within our policy design and

infrastructure management.

The thesis contains eight papers exploring the weather and climate impacts on individual

travel behaviour, each addressing a subset of this topic. Paper I explores the weather impact on

individual’s mode choice decisions. In paper II and III, individual’s daily activity time, number

of trips/trip chains, travel time and mode shares are jointly modelled. The results highlight the

importance of modelling activity-travel variables for different trip purposes respectively. Paper

IV develops a namely nested multivariate Tobit model to model activity time allocation trade-

offs. In paper V, the roles of weather on trip chaining complexity is explored. A thermal index is

introduced to better approximate the effects of the thermal environment. In paper VI, the role of

subjective weather perception is investigated. Results confirm that individuals with different

socio-demographics would have different subjective weather perception even given similar

weather conditions. Paper VII derives the marginal effects of weather variables on transport CO2

emissions. The findings show more CO2 emissions due to the warmer climate in the future.

Paper VIII summaries the existing findings in relations between weather variability and travel

behaviour, and critically assesses the methodological issues in previous studies.

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iv

SAMMANFATTNING

Mänsklig aktivitet producerar stora utsläpp av växthusgaser, vilket medför klimatpåverkan och

mer oförutsägbara väderförhållanden. Under det senaste årtiondet har extrema väderförhållanden

som kraftiga snöfall och översvämningar, lett till kaos och katastrofer i olika städer runt om i

världen, vilket kostat miljardtals euro. Å andra sidan påverkar den dagliga variationen av

väderleksförhållandena också individers dagliga resvanor. Denna påverkan, även om den är

mindre påfallande, kan ha stor effekt på transportsystemet. Därför har det blivit viktigt att kunna

förstå och förutsäga de möjliga individuella beteendeförändringarna med tanke på att klimatet

och väderleksförhållandena kommer bli mer oförutsägbara och skadliga.

Exempelvis har det svenska klimatet blivit varmare de senaste tio åren. Väder och klimatmönster

i södra och norra Sverige skiljer sig även signifikant ifrån varandra. Det är dock ovisst i vilken

grad förändrande väderförhållanden orsakar förändringar av resvanor. Att förstå dessa

mekanismer skulle hjälpa analytiker och politiker med att integrera lokala väder- och

klimatförhållandens unika karaktär i utformningen av policy och infrastrukturförvaltning.

Doktorsavhandlingen innehåller åtta artiklar som utforskar påverkan av väder och klimat på

individuella resvanor, var och en av dem utifrån olika perspektiv på detta ämne. Den första

artikeln utforskar påverkan av väderförhållanden på en individs färdmedelval, med fokus på

regionala skillnader. I den andra artikeln har individernas dagliga aktivitetstid, antalet

resor/kedjor av resor, restid och färdmedelval modellerats samtidigt med en

strukturekvationsmodell (SEM). Modellsystemet möjliggör för analytikerna att särskilja direkta

och indirekta effekter av varje vädervariabel. I tredje artikeln fokuserade analysen på

fritidsresenärer för att den andra artikeln visade att icke-pendlare är mer påverkade av

väderförhållanden. Ett simultant modellsystem har utvecklats för att modellera aktivitets- och

resmönster för icke-pendlare (ärenden såsom inköp eller fritid). Resultaten visar att det är viktigt

att modellera aktivitets- och resevariabler för olika ärenden. Fjärde artikeln utvecklar en ’nested

multivariate Tobit’-modell för att modellera avvägningar med hänsyn till tidsfördelningen för

olika aktiviteter en given dag. Jämfört med andra möjliga modellalternativ kan denna modell

beakta restid och färdmedelfördelningen som sekventiella och slumpmässiga komponenter som

påverkar tidsfördelningen mellan olika aktiviteter. Resultaten visar att denna modell har en bättre

prognosförmåga än traditionella modeller. I femte artikeln utforskas samspelet mellan väder och

reskedjornas komplexitet. Ett termiskt index har introducerats för att göra effekterna av den

termiska omgivningen mer exakta medan den rumsliga heterogeniteten av effekten av detta

termiska index har utforskats med en rumslig expansionsmetod. Resultaten visar att den rumsliga

fördelningen av effekten av termiskt index inte är likformig och att trenden skiljer sig åt

beroende på det viktigaste ärendet i reskedjan. I sjätte artikeln har rollen av uppfattningen av

väderförhållandena utforskats. Resultaten visar att individer med olika sociodemografiska

egenskaper har olika uppfattningar av väderförhållandena, även under likadana

väderförhållanden. Sjunde artikeln härleder marginaleffekterna av vädervariabler på

transportrelaterade CO2 utsläpp. Vädereffekterna på både individuella resvanor och

fordonsutsläppsfaktorer har tagits i beaktning. Resultaten påvisar ökade CO2 utsläpp på grund av

det varmare klimatet i framtiden. Åttonde artikeln sammanfattar de befintliga resultaten

avseende förbindelsen mellan vädervariation och resvanor och gör en kritisk bedömning av de

metodologiska frågeställningarna i tidigare studier. Olika riktningar för framtida forskning har

identifierats och föreslagits för att överbrygga klyftan mellan empiriska bevis och nuvarande

praxis i samhällsekonomiska kalkyler.

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ACKNOWLEDGEMENTS

When I was first interviewed by Professor Yusak Susilo who later on became my supervisor

in the PhD job interview, I only had a vague concept about travel behaviour and transport

modelling, but I thought it would be an interesting area to pursue my research. Thanks to my

supervisors, Professor Yusak Susilo and Professors Anders Karlström, trusting my ability and

encouraging me as an outsider in this field to pursue my research idea, I was able to start as a

PhD. student and now luckily reach this last mile of my doctoral study.

I would like to express my sincere thanks to my main supervisor Yusak who spent countless

time wondering around my office and discussing new ideas with me. Those discussions

eventually shaped my research ideas and direction. I also owe my deepest gratitude to Sisi, my

wife. She consoled and encouraged me when I was in my hardest time and, most important,

willingly took the responsibility for the home affairs. When I was very struggling with all the

new concepts and novel methods during my first year, my office roommate Dimas helped me a

lot. We had several discussions which helped me to understand and absorb those knew

knowledge. I would like to thank my research groupmates, Adrian, Roberto, Joram and Alin,

who often exchanged research ideas with me, especially Alin who also shared her data source

with me which enables me to further explore my research idea. Thanks to Qian from WSP for

working together on our research project. I also would like to thank the group of Phd students

working in Teknikringen 10, Anne, Masoud, Junchen, etc. You have not only provided valuable

help, but also interesting discussions and spare time that we spent together. Thanks Susanne, Per

and Gunilla for your help in various administration tasks. I also thank all the senior researchers

and professors in the division of Transport Planning, Economics and Engineering for the help

from time to time.

I would like to thank the Chinese Scholarship council for the financial support of my

doctoral study. Thank Dr. Yilin Sun from Zhejiang University for her information of this PhD

job position in KTH as well as her effort on our joint work.

Finally I would like to thank my parents and my other friends in China and Sweden for

everything.

Chengxi Liu

March 2016

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viii

LIST OF PAPERS

I. Liu, C., Susilo, Y. O, Karlström, A. (2015). The influence of weather characteristics variability on individual’s travel mode choice in different seasons and regions in Sweden.

Transport Policy, 41: 147-158. doi:10.1016/j.tranpol.2015.01.001

II. Liu, C., Susilo, Y. O, Karlström, A. (2015). Investigating the impacts of weather variability on individual’s daily activity–travel patterns: A comparison between

commuters and non-commuters in Sweden. Transportation Research Part A, 82: 47-64.

doi:10.1016/j.tra.2015.09.005

III. Liu, C., Susilo, Y. O., Karlström, A. (2014). Examining the impact of weather variability on non-commuters’ daily activity–travel patterns in different regions of Sweden. Journal

of Transport Geography, 39: 36-48. doi:10.1016/j.jtrangeo.2014.06.019

IV. Liu, C., Susilo, Y. O., Karlström, A. (2015). Jointly modelling individual's daily activity-travel time use and mode share by a nested multivariate Tobit model system.

Transportation Research Procedia: Papers selected for Poster Sessions at the 21st

International Symposium on Transportation and Traffic Theory, 9: 71-89.

doi:10.1016/j.trpro.2015.07.005. Submitted to Transportmetrica A.

V. Liu, C., Susilo, Y. O., Karlström, A. (2015). Measuring the impacts of weather variability on home-based trip chaining behaviour: a focus on spatial heterogeneity. Transportation,

1-25. doi:10.1007/s11116-015-9623-0

VI. Liu, C., Susilo, Y. O., Termida, N. A. (2016). Subjective perception towards uncertainty on weather conditions and its impact on out-of-home leisure activity participation

decisions. Paper presented at 6th International Symposium on Transportation Network

Reliability, Nara, Japan. Submitted to Transportmetrica B.

VII. Liu, C., Susilo, Y. O., Karlström, A. (2016). Estimating changes in transport CO2 emissions due to changes in weather and climate in Sweden. Presented at 95

th Annual

Meeting of the Transport Research Board, Submitted to Transportation Research Part D.

VIII. Liu, C., Susilo, Y. O., Karlström, A. (2016). Weather variability and travel behaviour - what do we know and what do we not know. Submitted to Transport Reviews.

http://dx.doi.org/10.1016/j.tranpol.2015.01.001http://dx.doi.org/10.1016/j.tra.2015.09.005http://dx.doi.org/10.1016/j.jtrangeo.2014.06.019http://www.sciencedirect.com/science/journal/23521465http://dx.doi.org/10.1016/j.trpro.2015.07.005

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MY CONTRIBUTION TO THE PAPERS

The idea of paper I was initiated from joint discussion between Professor Yusak Susilo and

Chengxi Liu. Chengxi Liu prepared the dataset, run the model, and wrote the paper. The

supervisors helped very much in revising Chengxi’ writing, interpreting the results and in

responding reviewers’ comments until the paper was accepted.

The idea of paper II was initiated from joint discussion between Professor Yusak Susilo and

Chengxi Liu. The model structure was adopted from Susilo and Kitamura (2008). Chengxi Liu

prepared the dataset, run the model, and wrote the paper. The supervisors helped very much in

revising Chengxi’ writing, interpreting the results and in responding reviewers’ comments until

the paper was accepted.

The idea of paper III, V, VI and VIII was initiated from joint discussion between Professor

Yusak Susilo and Chengxi Liu. Chengxi Liu prepared the dataset, run the model, and wrote the

paper. The supervisors helped very much in revising Chengxi’ writing, interpreting the results

and in responding reviewers’ comments until the paper was accepted.

The idea of paper IV was from Chengxi Liu. Chengxi Liu programmed the model, and

wrote the paper. The supervisors helped very much in revising Chengxi’ writing, interpreting the

results and in responding reviewers’ comments until the paper was accepted.

The idea of paper VII was initiated from joint discussion among Professor Yusak Susilo,

Chengxi Liu and Nursitihazlin Ahmad Termida. Nursitihazlin Ahmad Termida prepared the

dataset. Chengxi Liu developed and run the model, and wrote the paper. The supervisors helped

very much in revising Chengxi’ writing, and interpreting the results.

RELATED PUBLICATION, NOT INCLUDED IN THIS THESIS

IX. Susilo, Y. O., Liu, C. (2015). The influence of parents’ travel patterns, perceptions and residential self-selectivity to their children travel mode shares. Transportation, 43(2):

357-378. doi:10.1007/s11116-015-9579-0

X. Liu, C., Susilo, Y.O., Dharmowijoyo, D.B.E. (2015). Investigating inter-household interactions between individuals’ time and space constraints. Paper presented in 14

th

International Conference on Travel Behaviour Research (IATBR) in Windsor, 2015.

XI. Cats, O., Abenoza, R.F., Liu, C., Susilo, Y.O. (2015). Evolution of Satisfaction with Public Transport and Its Determinants in Sweden: Identifying Priority Areas.

Transportation Research Record: Journal of the Transportation Research Board 2538:

86–95

XII. Susilo, Y. O., Liu, C., Börjesson, M. (2016). Activity-travel pattern of different generations of Swedish women. Paper submitted to Transportation Research Part A.

XIII. Liu, C., Wang, Q., Susilo, Y. O. (2016). Assessing the impacts of collect-delivery points to individual’s activity-travel patterns: A greener last mile alternative? Paper accepted

for presentation in 14th World Conference on Transport Research (WCTR) in Shanghai,

2016.

XIV. Sun, Y., Liu, C., Chen, Y., Susilo, Y.O. (2016). The effect of residential housing policy on car ownership and trip chaining behaviour in Hangzhou, China. Submitted to

International Journal of Environmental Research and Public Health.

XV. Susilo, Y. O., Liu, C., Börjesson, M. (2016). How policies and values have changed activity-travel participations across gender, life-cycle, and generations in Sweden over

30 years. Paper submitted to Transportation Research Part A.

https://www.researchgate.net/journal/0361-1981_Transportation_Research_Record_Journal_of_the_Transportation_Research_Board

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XVI. Susilo, Y. O., Liu, C. (2016). Exploring the patterns of Time Use and Immobility in Bandung Metropolitan Area, Indonesia. Paper accepted for presentation in American

Geographer Annual Meeting (AAG) in San Francisco, 2016.

The author’s contribution in those publications except XI, was in data preparation,

modelling effort and writing the draft paper. The author’s contribution in XI was in modelling

effort.

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Table of Contents Abstract ................................................................................................................................... ii

SAMMANFATTNING .......................................................................................................... iv

ACKNOWLEDGEMENTS ................................................................................................... vi

LIST OF PAPERS ............................................................................................................... viii

MY CONTRIBUTION TO THE PAPERS............................................................................ ix

RELATED PUBLICATION, NOT INCLUDED IN THIS THESIS ..................................... ix

1 INTRODUCTION ............................................................................................................... 1

1.1 Climate change and travel behaviour ....................................................................... 1

1.2 Research objectives ....................................................................................................... 3

2 THEORETICAL BACKGROUND ..................................................................................... 5

2.1 The representation of weather in travel behaviour studies ............................................ 5

2.1.1 Weather variables as objective measures ............................................................... 5

2.1.2 Weather variables as subjective perceptions .......................................................... 6

2.2 The spatial and temporal variations of weather effects ................................................. 6

2.2.1 The spatial variation of weather effects ................................................................. 6

2.2.2 The temporal variation of weather effects.............................................................. 7

2.3 The dynamic and multi-dimensional nature of travel behaviour .................................. 7

2.3.1 The instrumental variable technique ...................................................................... 8

2.3.2 The multivariate modelling and sample selection model ....................................... 8

2.3.3 The dynamic nature of activity travel patterns ....................................................... 9

2.4 Externalities of weather effects ..................................................................................... 9

3 CONTRIBUTIONS ........................................................................................................... 10

3.1 The spatial and temporal variations of effects of objective weather measures ........... 11

3.2 Decomposing direct and indirect effects of weather ................................................... 11

3.3 The role of subjective weather perception .................................................................. 12

3.4 The marginal effects of weather on passenger transport CO2 emissions .................... 13

4 SIGNIFICANCE OF FINDINGS ...................................................................................... 13

5. LIMITATIONS AND FUTURE WORK ......................................................................... 15

REFERENCES ..................................................................................................................... 16

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1 INTRODUCTION

1.1 Climate change and travel behaviour

The period of the last three decades is likely to be the warmest three decades of the last

1400 years in the Northern Hemisphere. According to American National Centers for

Environmental Information (NOAA, 2015), July 2015 was the warmest month ever recorded for

the globe, as shown in Figure 1. Together with such a tremendous global warming phenomenon,

extreme weather conditions are becoming more frequent. In 2015, heavy snowfall in the eastern

coast of U.S., and heavy rain at the world’s driest dessert, Chile's Atacama Desert, those extreme

weather events cause huge loss in world’s most densely populated metropolitan areas as well as

world’s most depopulated areas.

Figure 1 Temperature percentiles recorded in July 2015 (NOAA, 2015)

On the other hand, transport system is one of the main contributors to climate change.

Transport is one of the major emitting sectors and the only sector that continues to grow

substantially (European Commission, 2015). Overall, the transport sector produces the second

largest share of CO2 emissions among all sectors in the EU, in which road transport, mainly by

passenger car, is responsible for around 70% of the total CO2 emissions in the transport sectors

(EU Transport in Figures, 2014). There is no doubt that human influence on the climate

system is clear, and recent anthropogenic emissions of greenhouse gases are the highest

in history. Recent climate changes have had widespread impacts on human and natural

systems (IPCC, 2016).

To the transport system, extreme weather events can cause critical failures at major

transport hubs and lead to disruptions of the whole transport system (e.g. Lam et al., 2008;

Fu et al., 2014). However, climate change and the change of everyday weather can also

lead to a gradual change of individual travel behaviour, thus lead to a gradual change in

travel demand (Koetse and Rietveld, 2009). In fact, the impact of everyday weather on

transport system, although is less noticeable than that of extreme weather events, can

have a strong influence on the transport system, since the variation of everyday weather

influences the travel patterns of each traveller throughout the year while extreme weather

may only affect a sub-region and its impact may only last for a short time period.

Understanding the change of travel behaviour given climate change and the change of

everyday weather is enormously important. As a change of everyday weather may

correspond to a marginal change in travel behaviour, those changes in travel behaviour

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would result in a fluctuation of travel demand in different weather conditions, and thus

contribute to various transport externalities, congestion, emission and traffic safety, etc.

Therefore, considering weather in travel behaviour studies can enhance the understanding

of how people travel across space and time. Incorporating weather elements in travel

demand models can improve the accuracy and prediction power of travel demand

forecasting, thus increase the robustness of decision support system for urban planning

and transport policies. Furthermore, given the warmer future climate, long term travel

demand forecasting without considering the impacts of climate change can potentially

over/under-estimate the future travel demand, thus may result in misleading policy

implications.

From a microscopic point of view, modelling travel behaviour with weather effects

replies on plausible assumptions on travel decision making mechanism, including how

travel behaviour should be modelled and how weather should be represented in travel

behaviour models. One the one hand, it is widely recognized that travel is a derived

demand; derived from the need to pursue activities distributed in space (Axhausen and

Gärling 1992). Activity-based models try to model travel behaviour as a result of activity

participation and activity sequencing. Modelling weather impacts in an activity-based

model framework can better reveal the actual roles of weather on travel behaviour than

modelling weather impacts in a trip-based modelling framework. However, existing

weather studies on travel behaviour mostly adopted a trip-based model framework.

On the other hand, weather in most existing travel behaviour studies is represented as

a series of objective variables, e.g. temperature, precipitation, relative humidity, etc., and

each has a single effect (see review: Böcker et al., 2013a). However, weather is

fundamentally a ‘subjective’ perception rather than an objective measure that affects

individual’s everyday travel decisions (e.g. Thorsson et al., 2004; Knez et al., 2009).

Objective weather variables in combination generate a micro-thermal environment and

such an environment is perceived by a given individual with a specific socio-

demographic profile. In travel behaviour research, only few studies used combined

weather variables to represent the thermal environment (one example: Cremmers et al.,

2015), while almost no studies used subjective weather measures. Different weather

representations in travel behaviour modelling can affect the spatial transferability of the

modelling results. For instance, the estimated effects of objective weather variables from

studies conducted in tropical countries may not be transferrable to those from studies

conducted in Nordic countries. Using subjective weather perception measures may help

explain the spatial heterogeneity of weather effects, since subjective weather measures

already take into account individuals’ “indifference weather condition” as the reference

point while the individual heterogeneity on weather perception that contributes to the

problem of spatial transferability is mostly reflected and captured in the heterogeneity of

reference point in relation to observed weather variables. For instance, 20 °C may be perceived cool for an individual from Indonesia who gets used to tropical climate, while

20 °C may be perceived warm for an individual from Sweden who rarely experiences hot climate. This heterogeneity in terms of weather reference point may lead to differences in

the effects of objective weather measures on travel behaviour, but can be captured when

using subjective weather perception measures.

Furthermore, most existing weather related studies focus on travel behaviour and

travel demand, while little attention is paid to the related transport externalities, such as

emissions, traffic safety, health effects, etc. Since weather does correspond to the change

in travel behaviour and travel demand, it is expected that the transport related

externalities are also influenced by the change of weather and climate. Deriving the

marginal effects of weather and climate change on externalities is essential for policy

makers to better assess transport policies related to those externalities.

With these research gaps in mind, this thesis reflects an investigation of the effects of

weather and climate on individuals’ activity participation and travel behaviour in a

holistic way. This research aim is further decomposed into four main research questions

in this thesis:

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1) How large are the spatial variations of the effects of objective weather variables on activity travel patterns?

2) What are the effects of weather on activity participations and travel behaviour considering travel as a derived demand of activity participation?

3) What are the roles of subjective weather perception and the heterogeneity of individual reference point of weather perception?

4) How large are the changes in transport externalities due to the climate change?

As this thesis has been submitted in the form of a PhD Thesis by Publication, a large

proportion of the content included is in the form of research articles that have already

been accepted or submitted for publication. This introductory chapter provides an

overview of those publications and how those publications fit together to answer the

research questions listed above.

1.2 Research objectives

The thesis utilizes two data sources. The first one is the Swedish nation travel survey (NTS)

with data ranging from 1994 to 2011 (Algers, 2001), matched with weather data from the

Swedish meteorological and hydrological institute (SMHI). The daily measured and three-hour

measured weather data was assigned to each trip by matching the weather data from the weather

station nearest to the departure point of the trip and selecting the weather variable with the

measured time closest to the departure time. The average distance from the weather station to its

corresponding city centre is around 10 km. The limitation for combining two datasets is that

distances from a nearest weather station to respondent’s departure place vary for each trip,

raising questions about to which degree weather conditions measured at weather station are the

same as those at the departure place. However, if the travel behaviour variables of interest are in

daily level (mostly in all eight papers), the effect of these uncertainty and variability are reduced.

Compared to other similar studies, the NTS data combined with SMHI weather data cover a

longer period and broader region, 1994 to 2001, 2003 to 2004, 2005 to 2006 and 2011,

throughout all Sweden’s regions. This advantage enables us to explore weather impacts more

deeply in both space and time dimensions.

The second dataset applied in this thesis is a longitudinal travel survey data collected in

Solna municipality, Stockholm. The survey used a self-reported two-week travel diary via paper

and pencil in four waves. The implementation of this panel survey covered an overall length of

seven months starting from October 2013 to June 2014. The first wave (14th-27

th October, 2013)

travel survey took place just before the opening date (28th October, 2013) of the new tram line

extension. The following waves took place afterwards. In this thesis, only the travel diary data

from third (17th-30

th March, 2014) and fourth (26

th May-8

th June, 2014) waves were used which

are 5 months after the opening date of the tram line extension. Thus the influence of the newly

extended tram line on individuals’ travel behaviour change was believed negligible. Besides, no

major infrastructure change took place from wave 3 to wave 4 periods. Moreover, this seven-

month period minimises an issue of sample aging when implementing a panel survey (Raimond

and Hensher, 1997). Totally 67 individuals participated in all waves and 75 individuals

participated in wave 3 and wave 4. Detailed description of this panel survey can be found in

Termida et al. (2016). In this survey, two weather related questions were asked for each

respondent every day during these two-week survey periods. The first one is: how did the

weather make you feel on the given day? A 5 point Likert scale measure ranges from “very

disappointed” to “very satisfied”. Another issue is that each weather question was answered once

per day as it was attached in the travel diary survey. Thus subjective weather perception was

only available at daily level. Although the weather perception surely varies in different time of a

day according to the weather condition at the given time of a day, it is practically difficult and

expensive to obtain the subjective weather perception multiple times in a day. However, given

the fact that subjective weather perception is typically qualitative and ordinal, it is assumed that

the obtained Likert scale score represents the respondent’s perception of the overall weather on

the given day. Thus, the subjective weather perception at daily level can still be interpreted as

4

the subjective feelings towards weather in an overall day.

To answer the research questions, a series of research objectives are discussed and

addressed in the publications.

Research objective 1: to examine spatial and temporal heterogeneity of the effects of

objective weather variables in activity participation, mode choice and trip chaining. (Paper

I, Paper III and Paper V)

Paper I aims to examine the impacts of objective weather variables on individuals’ mode

choice in different regions of Sweden with different climates. Paper III explores the interactions

between time allocation, travel demand and mode choice under different weather conditions in

different regions of Sweden. Paper V focuses on the impacts of weather on trip chaining

complexity and adopts a spatial expansion method to account for spatial heterogeneity between

municipalities. Understanding these would be crucial in producing a better understanding and

forecasting the travel demand within the transport network and in determining the most suitable

transport policy that can be implemented during particular weather conditions for particular

geographical locations in particular seasons.

Research objective 2: to examine the weather impact and the climate impact when

several activity-travel variables are jointly modelled. (Paper II, Paper III and Paper IV)

Paper II aims to analyse the impacts of weather variability on the individual’s daily activity-

travel engagement, including activity time use, trip frequency, trip chaining, travel time and

mode share. Paper III adopts a similar model structure but focuses more on the non-commuters

who are found to be more elastic to the change of weather and climate. Paper IV develops a

multivariate sample-selection model to model activity time allocation problem. Modelling

activity participation and travel behaviour jointly provides a more comprehensive insight into

weather and climate impacts than studies that only focus on one activity-travel variable.

Research objective 3: to understand the role of subjective weather perception and the

heterogeneity of individual reference points of weather perception. (Paper VI)

Paper VI aims at exploring the heterogeneity in the reference points of weather perception

for individuals from different socio-demographic groups, and exploring the role of subjective

weather perceptions affecting individuals’ leisure activity participation decisions. Exploring how

individuals perceive weather and utilizing their weather perception in travel choices is essential

to better understand the underlying reason of spatial and temporal heterogeneity of weather

effects.

Research objective 4: to estimate the marginal change of externalities due to the

change of weather and climate. (Paper VII)

Paper VII adopts a model structure to model the impacts of weather and also considers the

impacts of weather on emission factors of vehicles. The marginal effects of weather on transport

CO2 emissions are derived and the results can be useful in emission policies and cost-benefit

analysis.

Research objective 5: to provide a summary and assess the methodological issues on

modelling weather in travel behaviour studies. (Paper VIII)

Although the research questions listed above will be addressed in this thesis, the results

shown above also propose further research questions. Therefore, Paper VIII summarizes the

existing findings in relations between weather variability and travel behaviour, and critically

assesses the methodological issues in those studies. Several further research directions are

5

identified and suggested for bridging the gap between empirical evidence and current practice in

cost-benefit analysis.

2 THEORETICAL BACKGROUND

2.1 The representation of weather in travel behaviour studies

2.1.1 Weather variables as objective measures

So far most of the previous studies have investigated the relationship between objective

weather measures and travel behaviour variables. The spatial and temporal matching between the

weather information and the observed trips is often the first research question aroused in the data

preparation stage. Most studies assigned weather information to each trip by matching the

meteorological indicators from the weather station closest to the departure point of the trip and

selecting the weather variable with the measured time closest to the departure time. By doing

this, it is implicitly assumed that each traveller would base his or her travel decision on the

weather condition that is prevailing at the departure place and time. In many cases, weather

stations that record weather information can be sparse relative to the spatial distribution of trip

occurrence. Therefore, various interpolation methods have been used to assign the most

“accurate” weather information to each trip occurrence location by assuming a certain spatial

distribution of the meteorological indicators. For instance, Chen and Mahmassani (2015)

interpolated a smooth function over the study area based on the observed meteorological

indicators at the stations. Jaroszweski and McNamara (2014) employed a weather radar approach,

though their focus is on traffic accidents. Nevertheless, the use of different interpolation methods

is to better reflect the actual weather condition at a given specific location.

Another issue is whether to match the weather information to the departure time and

location, arrival time and location, or a certain period of time during the trip. Chen and Clifton

(2011) argued that travellers would assess the weather conditions based on the conditions prior

to travel. Chen and Mahmassani (2015) assumed travellers would consider the weather

conditions that are in a near future, and they matched weather data according to the destination

location and time of each trip, although they also admitted that “more research is needed to

examine how weather is incorporated in travel decision processes”. Based on a stated preference

survey, Cools and Creemers (2013) showed that “planned” travel decisions are often altered by a

last-minute change in response to adverse weather conditions at the time of departure. Sihvola

(2009) interviewed the car drivers and also found a substantial amount of drivers changing travel

plans (e.g. change route or departure time). Those findings do not suggest a uniform solution but

point out the need for deeper understanding of the role of weather in the travel decision making

process.

Weather information from weather stations is often in the form of meteorological measures

such as max/min/average temperature, wind speed, relative humidity and precipitation, etc.

Those meteorological measures are often directly adopted into the travel behaviour studies in

representing weather (Böcker et al., 2013a). This implicitly assumes that each meteorological

indicator has a single and independent effect on the travel behaviour indicator of interest.

However, different meteorological measures often co-occur, indicating that the effect of each

meteorological measure on travel pattern is interrelated. Phung and Rose (2008) found a

combined negative effect of wind and light rain on bicycle counts. A few weather studies in

travel behaviour used data mining techniques to classify distinct weather types. For instance,

Clifton et al. (2011) used a two-step clustering technique to classify various types of weather

conditions based on observed meteorological indicators. Creemers et al. (2015) pioneered

weather related travel behaviour studies by introducing different thermal comfort constructs and

comparing their performance in travel behaviour models. However, machine learning methods to

classify weather types and thermal comfort measures have not been widely used in most travel

behaviour studies related to weather.

6

2.1.2 Weather variables as subjective perceptions

Despite the fact that the concept of perceived weather conditions have been used in many

psychological studies (e.g. Thorsson et al., 2007; Thorsson et al., 2011; Connolly, 2013),

subjective weather perception measures are rarely introduced in travel behaviour studies. Böcker,

et al. (2015) included a measure of perceived temperature and showed that weather-exposed

cyclists experience thermal conditions as significantly colder than the more weather-protected

users of motorised transport modes. Subjective weather measures have several theoretical

advantages compared to objective weather measures. Weather is fundamentally a subjective

perception that is perceived by an individual and used in his/her travel decision making process.

Individuals with different socio-demographics, social-cultural contexts, can have different

subjective weather perceptions even under the same weather conditions (Knez et al., 2009).

Theoretically, using subjective weather perception provides more accurate and interpretable

weather effects than using objective weather measures, since the subjective weather measures

naturally define an individual’s reference point of his/her perceived weather, which is usually

latent/unobserved when objective weather measures are used. The subjective weather measures

serve as mediation factors to bridge objective weather measures and travel behaviour. The

relationship between objective weather measures and subjective weather measures reveals the

individual heterogeneity in perceiving weather. The relationship between subjective weather

measures and travel behaviour reveals the roles of weather in one’s travel decision making

process.

Subjective weather measures are also believed to be interrelated with other psychological

constructs such as moods and happiness. Psychologists tend to explore the role of weather on

well-being while treating travel patterns as mediating effects (see reviews, Kööts, et al., 2011),

but travel behaviour analysts tend to explore the role of weather on travel patterns while treating

well-being as control variables. After all, it is plausible that the relationships among weather,

psychological constructs and travel behaviour are mutually interacted. One’s travel behaviour is

not only influenced by weather but also affects his/her subjective weather perception (e.g. one

who is immobile on the given day may not experience the outdoor weather and thus is more

likely to have a neutral subjective weather perception on that day). Understanding these complex

relationships would be vital for capturing the actual role of weather on travel behaviour and thus

improving the prediction power of travel demand forecasting in a possible warmer future

climate.

2.2 The spatial and temporal variations of weather effects

2.2.1 The spatial variation of weather effects

When objective weather measures are used in travel behaviour models, as in most empirical

studies, it is often stated as “the results from this case study may not be transferrable to other

countries, regions”. This spatial variation of weather effects on travel behaviour can be attributed

to various reasons, such as geographical difference, climate difference and population difference,

etc., for any two different locations.

Weather conditions and built environment are intrinsically interrelated and together form

the microclimate at a specific location (Theeuwes, et al., 2014). Compared to natural areas,

densely built urban areas cool less effectively at night due to heat stored in building surfaces and

reduced radiation into the open sky, while warming up more quickly during the day due to

multiple reflections of solar radiation, heated building surfaces, and a lack of evapotranspiration

from green plants, although the warming up may be counteracted by building shadows (Böcker,

2014). Therefore, individuals travelling over space-time in areas with different built

environments may experience different microclimates even with the same meteorological

measures, thus lead to different travel decisions. From a macro level point of view, cities and

regions with distinct built environment characteristics may exhibit clear weather effects as

distinct microclimates are formed and perceived by travellers.

The effects of weather on travel behaviour may also differ between countries with distinct

7

climate and culture, and therefore are climate and culture dependent. Local residents in countries

with distinct climate and culture may adapt their clothing type, lifestyle, activity participation

and travel behaviour to the local climate according to the culture and social norm, thus they may

response differently to a change of a weather variable in different variable intervals. For example,

in Scandinavia, cold weather and short days limit the time spent outdoors during large portions

of the year. In Tokyo, Japan, however, cold weather is seldom a problem. Meanwhile, the

Scandinavian ideal of beauty has included a suntan, whereas in Asia, the ideal of beauty is fair

skin. The above examples can explain the fact that most people in northern Europe automatically

choose a place in the sun, whereas Japanese people tend to avoid the sun to a greater extent

(Thorsson et al., 2007).

Different population structure may also contribute to the spatial variation of weather effects.

Individuals with different socio-demographic profiles may have different space-time constraints

(Hägerstrand, 1970) and health conditions due to individuals’ different personal and social roles.

E.g. elders may have more free time but are limited by their physical abilities, and thus they may

not respond to a good weather. On an aggregated level, the impact of weather in a region with an

aging population would therefore differ from that in a region with a young population.

2.2.2 The temporal variation of weather effects.

Even for a given region, the weather effects may not be the same in different time of a year.

For instance, 10°C in summer in a Nordic country may have a completely different effect as

10°C in winter in that country. The former may be interpreted as “cold in summer” while the

latter may be interpreted as “warm in winter”. This is because individuals have different weather

adaptations and different standards (reference point) of “comfort” weather in different seasons.

Hassan and Barker (1999) were among those who first tried to investigate the unseasonable

weather impact on traffic. They defined the unseasonable weather as those with largest

difference between observed and historically mean weather variables. Sabir (2011) used seasonal

dummies together with objective weather variables in the travel behaviour model and found

significant effects of seasonal dummies. These findings indicate that the estimated variable

effects of weather may be different in different seasons.

On the other hand, the weather effects for a given region may also differ in different years,

since the climate is changing dynamically so as people’s weather adaptation and reference points

of subjective weather perception. In Sweden, the seasonal mean temperature and precipitation

have increased continuously from 1994 to 2011. It is plausible that Swedish citizens are

gradually adapting to the new climate reflected in the change of their travel behaviour, although

the magnitude may be small.

2.3 The dynamic and multi-dimensional nature of travel behaviour

The travel choices are often made jointly, which indicates a complicated travel decision

making process. For instance, trip chaining choice is often made jointly with mode choice which

is subject to the destination of each trip in the trip chain. Those who choose to drive a car are

more likely to do complex trip chaining and visit distant destinations (Ye et al., 2007). Travel

choices are often treated and modelled as conditional choices subject to the activity participation

choices, including activity location choices (e.g. Bhat and Singh, 2000), activity timing (e.g.

Habib et al., 2009), and activity duration (e.g. Lee, et al., 2009), etc. Travel choices are also

referred to short term choices and are subject to the long term choices such as car ownership and

residential location choices (e.g. Cao et al., 2007 and Noland, 2010). Recently, Zhang (2014)

also argued that people’s long term life-cycle choices would be a long term choice relative to

residential location choice and car ownership choice.

To model activity participation and travel choices, model frameworks that can handle

multiple dependent variables of different types (categorical, continuous, counts etc.) are needed.

Moreover, given the dynamic nature of activity participation, models that can handle state

dependence are also discussed. The next subsections focus on summarizing the methodological

issues on modelling activity travel variables.

8

2.3.1 The instrumental variable technique

Instrumental variable technique (IV) is used extensively in capturing the endogeneity effect

of residential location on travel behaviour (see review: Mokhtarian and Cao, 2008). A simple

model with two dependent variables, y1 and y2, with one as the instrumental variable can be

expressed as:

𝑦1 = 𝑓(𝑋1) + 𝜇

𝑦2 = 𝑔(𝑦1̂, 𝑋2) + 𝜀 (1)

In the equation above, the equation of y1 first models y1 as a function of instrumental

variables X1. It is assumed in IV models that X1 is not correlated with ε. The equation of y2 then

is modelled as a function of exogenous variables X2 and the predicted value of y1, 𝑦1̂. Since X1 is by assumption uncorrelated with ε, the predicted value 𝑦1̂.as a function of X1 is also uncorrelated with ε. IV technique is often used to model continuous dependent variables but very few in

modelling discrete dependent variables (Bhat and Guo, 2007). Lee (2007, 2009) used a Tobit

model with IV to examine the determinants of activity time allocation and the relationship

between activity duration and travel time. In weather related travel behaviour research, the IV

technique can be a useful approach, since weather can potentially be variables in X1 which serve

as instrumental variables. For instance, weather can affect travel time to shopping (y1) (e.g. due

to the change of travel mode, etc.) thus leading to a change in shopping duration (y2).

However, the limitation of IV technique is also well-known. The requirement of IV is in

itself a contradiction. First, 𝑦1̂ must not be significantly correlated with ε. Second, 𝑦1̂ must be significantly correlated with y1 to control for endogeneity. However, not only finding suitable X1

with which to model y1 can be difficult, but also modelling y1 as a function of X1 which is

uncorrelated with ε will necessarily leave a substantial amount of variance in y1 unexplained,

therefore making 𝑦1̂ less correlated with y1 (Mokhtarian and Cao 2007). Such limitation may potentially yield inconsistent estimation in IV model.

2.3.2 The multivariate modelling and sample selection model

IV, although is computationally simple, has its limitation as discussed above (difficult in

finding perfect IV and not applicable when the dependent variables are discrete). The sample

selection model (Heckman, 1990) is often used as an alternative approach, although is more

computationally demanding. A typical sample selection model can be expressed as:

𝑦1

∗ = 𝑓(𝑋1) + 𝜇

𝑦1 = 0 if 𝑦1∗ < 0; 𝑦1 = 1 if 𝑦1

∗ ≥ 0

𝑦2 = 𝑔1(𝑋2) + 𝜀 if 𝑦1 = 0

𝑦2 = 𝑔2(𝑋2) + 𝜀 if 𝑦1 = 1

(2)

The equation of 𝑦1∗ is often called selection model. In Eq.(2), the selection model has a

binary outcome, either 1 (e.g. choosing to travel by car) or 0 (e.g. choosing not to travel by car).

However, the selection model can have a multinomial outcome (e.g. choosing among walk, bike,

car and public transport) or an ordered outcome (e.g. number of trips travelled in a given day).

The parameters and functional form of the outcome model, y2, is dependent on the outcome of

the selection model, y1. It is worth noting that if all variables in X1 do not appear in X2, such a

sample selection model is essentially an IV model.

The sample selection part can be extended into a multivariate case. In that sense, the

selection model is not one equation, but multiple equations set up jointly. For instance, the

selection model can be three equations of binary activity participation, whether to play football

on a given day, whether to go to gym on a given day and whether to go shopping on a given day.

Each selection model is associated with an outcome model, the activity duration of

football/gym/shopping on that day. The multivariate sample selection model not only captures

9

the sequential nature of activity participation and travel which is specified by sample selection,

but also captures the interdependencies and trade-offs in activity participation with different

purposes as specified in the multivariate modelling. In the context of weather related travel

behaviour studies, weather can potentially affect the activity participation and time allocations,

e.g. reducing the number of outdoor leisure activity trips and outdoor leisure activity time in bad

weather condition, which may therefore increases the time spent on in-home leisure activities

(multivariate nature of activity participation). This would therefore decrease the travel time for

outdoor leisure activities (the outcome model of a selection model of outdoor leisure activity

participation). Adopting this modelling framework would provide more precise and realistic

marginal effects and elasticities of variable effects compared to models with one single

dependent variable (Louviere et al., 2005).

2.3.3 The dynamic nature of activity travel patterns

Activity participation, especially non-mandatory activity participation shows a clear

dynamic nature. A dynamic travel behaviour model will use time as a dimension and travel

choices at a given point in time will be dependent on earlier conditions and events. The previous

activity participations that influence the current activity participation decisions are often known

as state-dependence or habit persistence (e.g. Ramadurai and Srinivasan, 2006; Arentze and

Timmermans, 2009). For instance, if one has a habit of going to gym, observing this individual

being in the gym on previous days would plausibly have a positive effect on observing this

individual being in the gym on the given day. On the other hand, if one is an elder, observing

this individual being in the gym on previous days would likely to have a negative effect on

observing this individual being in the gym on the given day.

When investigating the weather impact, it is expected that weather would have a more

prominent effect on leisure (non-mandatory) activity participation which clearly exhibits the

dynamic nature. Modelling weather in the dynamic model framework is important and essential

for deriving precise and realistic effects of weather. However, in order to capture the dynamic

behaviour, longitudinal data collection is required. Given that most weather related travel

behaviour studies have used cross-sectional data, the dynamic nature is often ignored in the

analysis, and therefore, the estimated effects of weather on leisure activity participation may be

biased (e.g. the estimated effects of weather may be too low if a considerable amount of

respondents had leisure activities on the previous days and thus they need to have a rest on the

observed day).

2.4 Externalities of weather effects

The impacts of weather on travel behaviour have profound consequences on travel related

externalities, including transport emissions, congestion, traffic safety, etc. For instance, if a

considerable amount of travellers travel longer distances in a warmer future climate (Böcker et

al., 2013b), more emissions from passenger transport are expected in such a future climate. A

more realistic behavioural model on activity participation and travel are the foundations of

accurately assessing transport externalities. However, despite plenty evidences of the impacts of

weather on travel demand, the traditional travel demand-supply models (four-step model) which

often serve as a main tool in transport appraisals for calculating the transport externalities often

do not consider the demand variation due to changes in weather. As the models are calibrated by

travel survey data in which both good weather and adverse weather conditions are sampled, the

predicted or simulated travel choices are neither interpreted as travel choices in good weather

nor as travel choices in bad weather, but a kind of average of travel choices in good and bad

weather depending on the proportions of samples under good/bad weather. The corresponding

aggregated measures from the models, e.g. mode share and Origin-Destination demand, are also

not aggregated measures in good/bad weather.

Although most of the travel demand models focus more on peak-hour traffic or commute

trips which are less affected by weather, many of those models also modelled leisure trip

demand (for instance, Sampers in Sweden, Algers et al., 2009). However, to what extent the

model components of leisure trip demand are biased due to the absence of weather is largely

10

unknown, so do the policy evaluations related to those modelled leisure trips. This calls for a

need to utilize the knowledge gained in weather related travel behaviour studies and apply them

in travel demand forecasting and externality assessment. The knowledge would help transport

planners and policy makers better understand the relevant externalities that are due to the change

of weather and climate.

3 CONTRIBUTIONS

Although papers included in this thesis represent independent and stand-alone research

articles, these papers follow a logical order. Figure 2 presents the common and distinguishing

elements among paper I to VII. Paper VIII provides a review on the weather related topics. As

shown in Figure 2, both objective and subjective weather measures are used in the articles. Paper

I, II, III, IV and VII used meteorological measures, in which climate and weather effects are

separated and the interaction effect between these two effects are also considered. Paper V used

a thermal index, Universal Thermal Climate Index (UTCI), to represent the thermal environment.

Paper VI adopted the subjective weather measure which is a 5 point Likert scale measure.

Several modelling techniques are used. Benchmark models such as multinomial Logit model and

ordered Probit model are used to explore the spatial variation of weather effects (Paper I and V),

in which Paper I focuses on model choice while Paper V looks at trip chaining complexity.

Instrumental variable technique and sample selection model are developed to model activity

participation and travel behaviour jointly. The direct, indirect and total effects of weather

variables are presented (Paper II, III, IV and VII). Activity duration, activity participation, trip

chaining, mode choice and travel time are jointly modelled. Paper VI develops a dynamic

ordered Probit model to capture the influence of previous days’ leisure activity travel on the

leisure activity travel on a given day. Paper VII builds upon the knowledge from previous papers

and further derives the marginal effects of weather on passenger transport CO2 emissions.

Figure 2. Overall linkage of research articles

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3.1 The spatial and temporal variations of effects of objective weather measures

Paper I investigates the spatial and seasonal heterogeneity of the effects of objective

weather measures on mode choice. 12 sub-models were estimated for each combination of

seasons (spring, summer, autumn and winter) and regions (southern, central and northern

Sweden). In general, the effects of weather variables differ in different seasons and different

regions. The possibility of individuals choosing walk and public transport is higher in winter

compared to summer, while the possibility of individuals choosing cycling is lower in winter

compared to summer. Temperature effect is represented as a measure relative to the monthly

mean temperature at a given municipality. People in the central and southern Sweden tend to

walk less in abnormal (different than is expected on each corresponding season and region)

temperature conditions in summer, while people in the northern Sweden tend to walk more in

these situations. Northern travelers walk less when transformed temperature shifts from ‘very

cold’ to ‘very warm’ but this is not the case in the central and southern Sweden. Similarly,

northern Sweden cyclists are more aware of temperature variations than cyclists in the central

and southern Sweden in spring and autumn when temperature changes significantly.

Precipitation is negatively correlated with walk share in winter while surprisingly becoming

positive in summer. One possible explanation for this observation is that precipitation also leads

to a shortening of trip distance in summer. The results reveal the need of jointly considering

several activity-travel indicators in a holistic model framework. The findings also highlight the

importance to incorporate individual and regional unique anticipation and adaptations

behaviours within our policy design and infrastructure management.

Paper III used the instrumental variable technique (Simultaneous Tobit model) to explore

the regional difference of the effects of objective weather measures on non-commuters’ activity-

travel pattern. The model structure explicitly classifies activity-travel indicators into routine and

leisure activity purposes and allows for mutually endogenous relationships between activity-

travel indicators of those two activity purposes. Such a model structure depicts a more

comprehensive picture of weather effects on individuals’ activity-travel patterns on a given day

compared to independent modelling of activity-travel indicators. The model results confirm that

regional differences between weather effects are substantial due to differences in direct, indirect

and total marginal effects. A regional difference observed in total marginal effects can be the

result of different decompositions in direct and indirect marginal effects in different regions. For

instance, the increase of monthly mean temperature has direct negative effects on routine activity

duration and number of routine trips in southern and central Sweden. But those negative effects

turn positive in northern Sweden. Between-municipality variability constitutes a considerable

part of the variability in activity duration and travel time. Between-municipality variability in

leisure activity duration and leisure travel time is larger in northern Sweden, while that of routine

activity duration and routine travel time is larger in central Sweden, after weather and socio-

demographic variables have been controlled.

Paper V focuses on the spatial and seasonal variation of the effects of the thermal index,

Universal Thermal Climate Index (UTCI). Thermal index adopts existing knowledge in

biometeorology and provides a combined effect of temperature, wind speed and relative

humidity to represent the thermal environment given the weather condition. The results reveal

that the variation of UTCI significantly influences trip chaining complexity in summer and

autumn but not in spring and winter. The routine trip chains are found to be most elastic towards

the variation of UTCI. The marginal effects of UTCI on the expected number of trips per routine

trip chain range from -0.045 trips to 0.040 trips in summer and autumn in different

municipalities, which indicates substantial spatial variations. There are clear differences in the

marginal effect estimates between the model with weather variables and the model without

weather variables. These differences in the marginal effects can reach 50 % and even higher.

These findings also indicate that transport policies aimed at trip chaining behaviour must also be

localised to incorporate the local climate.

3.2 Decomposing direct and indirect effects of weather

Paper II investigated the commuters’ and non-commuters’ activity-travel pattern using

instrumental variable techniques. Non-work activity duration, number of trips and trip chains per

12

day, travel time and mode share are jointly modelled within the Structural Equation modelling

(SEM) framework. The analysis results show that the effects of weather can be even more

extreme when considering indirect effects from other travel behaviour indicators involved in the

decision-making processes. Commuters are shown to be much less sensitive to weather changes

than non-commuters. Variation of monthly average temperature is shown to play a more

important role in influencing individual travel behaviour than variation of daily temperature

relative to its monthly mean, whilst in the short term, individual activity–travel choices are

shown to be more sensitive to the daily variation of the relative humidity and wind speed relative

to the monthly mean. Poor visibility and heavy rain are shown to strongly discourage the

intention to travel, leading to a reduction in non-work activity duration, travel time and the

number of trips on the given day.

Paper III focuses on the non-commuters and explicitly models the trade-offs between

routine and leisure activity-travel pattern. The impacts of weather on the trade-off between

routine and leisure activities are found. Weather effects are far from simple, strongly influenced

by the mediation effects, especially with regards to travel time. A regional difference observed in

total marginal effects can be the result of different decompositions in direct and indirect

marginal effects in different regions. For instance, a colder-than-normal day has a positive direct

marginal effect on leisure activity duration, but a negative direct marginal effect on leisure trips.

This implies that although Swedish non-commuters have a tendency to travel less on a colder-

than-normal day, they might spend more time per leisure activity. Similarly, a warmer-than-

normal day has a negative direct marginal effect on leisure activity duration, but the effect is

partially offset by the positive indirect marginal effect from routine activity duration.

These findings depict a more comprehensive picture of weather impact compared to

previous studies and highlight the importance of considering interdependencies of activity travel

indicators when evaluating weather impacts.

Paper IV develops a nested multivariate Tobit model which is a multivariate extension of a

sample selection model. The model, instead of treating endogeneity effects as instrumental

variables, models travel time as a sequential outcome conditional on the participation of activity.

Compared to the instrumental variable models (Paper II and Paper III), this model framework is

typically useful in prediction but not for revealing the heterogeneous pattern. The findings

suggest a potential positive utility of travel time added on non-work activity time allocation in

the Swedish case. Meanwhile, the results also show a consistent mode choice preference for a

given individual. The estimated nested multivariate Tobit model provides a superior prediction,

in terms of the deviation of the predicted value against the actual value conditional on the correct

prediction regarding censored and non-censored, compared to mutually independent Tobit

models. However, the nested multivariate Tobit model does not necessarily have a better

prediction for model components regarding non-work related activities.

3.3 The role of subjective weather perception

Paper VI investigates the role of subjective weather perception. Two research questions are

addressed. 1. How individuals from different socio-demographic groups perceive weather. 2.

How subjective weather perceptions affect individuals’ leisure activity participation decisions.

The results show that the reference thermal environment in general corresponds to the historical

mean of the thermal environment. The effects of objective weather measures on subjective

weather perception vary substantially between individuals. Elders are more sensitive to the

change of thermal environment, compared to teenagers, due to their physical conditions.

Respondents from the household without children are in general more sensitive to the variation

of the Z score of UTCI compared to those with children. The high income group is in general

less sensitive to the variation of the Z score of UTCI compared to the low and medium income

groups. Respondents with children in their households tend to have a much lower subjective

weather scores in rainy conditions than those without children in their households.

Moreover, the effect of subjective weather perception on leisure activity participation is

non-linear and asymmetric. Only “very bad weather” and “very good weather” significantly

influence the leisure activity participation. The effect of “very bad weather” also varies

significantly between individuals. However, this heterogeneity cannot solely be attributed to the

13

socio-demographic variables, but more likely to be influenced by other unobserved social

psychological factors. The intra-individual heterogeneity in the effect of “very good weather”

has a smaller magnitude than that in the effect of “very bad weather”. The intra-individual

heterogeneity in the effect of “very good weather” can be explained mainly by socio-

demographic variables, while the intra-individual heterogeneity in the effect of “very bad

weather” cannot solely be attributed to socio-demographic variables but is more likely to be

influenced by other unobserved social psychological factors.

3.4 The marginal effects of weather on passenger transport CO2 emissions

Paper VII adopts the knowledge from the previous papers and explores the role of weather

variation on passenger transport CO2 emissions. Paper VII considers both the effects of weather

on travel pattern and the effects of weather on vehicle emission factors. The marginal effects of

weather on passenger transport CO2 emissions are derived. The results showed an increase in

individual CO2 emissions in a warmer climate and in more extreme temperature conditions (5%

CO2 emission increase given 2 °C warmer climate and 20% more extreme weather), whereas

increasing intensity of precipitation and snow corresponds to a slight decrease in individual CO2

emissions, although it may be due to the fact that only a few trips were sampled in those weather

conditions. Given that most large-scale transport demand–supply interaction models do not

consider the impacts of weather variability, using the estimated CO2 emissions from those

models as the estimates of future external effects may considerably underestimate the actual

future CO2 emissions. With global warming and more frequent adverse weather conditions, such

an underestimation may reach 8% or more.

4 SIGNIFICANCE OF FINDINGS

Understanding the roles of weather and climate on individuals’ travel behaviour is essential

for accurately predicting future travel demand in a possible warmer future climate. It would

provide various implications on transport policies designed to incorporate the global warming

and extreme weather events. To this extent, this thesis contributes to both the theoretical

understanding on how weather affects individuals’ activity-travel patterns and the practical

implication on transport policies regarding to climate and weather mitigation. Specifically, this

thesis focuses on the representation of weather and climate, travel behaviour modelling and

externalities, in order to better depict how the changes of weather and climate can affect

individuals’ activity participation and travel behaviour.

In general, the findings in this thesis are useful for both policy-makers and researchers in

the field of transport science, particularly in terms of travel behaviour and mobility. For

researchers, this thesis provides further insights on how different weather elements jointly affect

several travel behaviour variables by considering the direct, indirect and total effects. Besides,

the understanding of the role of subjective weather perception provides further insights on how

weather plays a role in the travel decision making process, compared to case-specific empirical

assessment of the effects of weather. For policy makers, the regional difference of weather

impacts found in this thesis highlights the importance of incorporating individual and regional

unique anticipation and adaptations behaviours within our policy design and infrastructure

management. The findings in Paper V also highlight the potential biasness of marginal effect

estimates of other explanatory variables if weather effects are ignored, leading to potentially

misleading policy implications. Findings in Paper VII show an increasing CO2 emission in a

future warmer climate, and the findings are useful for climate mitigation policies. Compared to

previous studies that have investigated the impacts of weather on travel behaviour, this thesis

provides deeper insights compared to previous studies from the following aspects.

By exploring the spatial and temporal heterogeneity of the impacts of weather on activity

participation and travel behaviour, it is found that the impacts of weather and climate differ in

different seasons and different regions for commuters and non-commuters respectively. The

effects differ not only in terms of magnitude but even the sign of the effect. For instance,

precipitation is negatively correlated with walk share in winter while surprisingly becoming

positive in summer. Therefore, results from one specific region and time of a year cannot be

14

directly adopted into another region and different time of a year. It is crucial to incorporate and

anticipate the uniqueness of weather impacts for a given region and season within our policy

design and infrastructure management. For instance, the travel demand of leisure activity travel

is expected increasing in southern Sweden (marginal increase in number of leisure trips per

individual per day is 0.31) and central Sweden (marginal increase in number of leisure trips per

individual per day is 0.46) in snow covered situation, while the opposite is true in northern

Sweden (marginal increase in number of leisure trips per individual per day is -0.54) (Paper III).

Therefore, road maintenance on snowy days and after snowy days, especially for roads

connecting to leisure activity facilities, is important in southern and central Sweden.

By using instrumental variable techniques (paper II and III) and sample selection model

(Paper IV), the direct, indirect and total effects of weather variables on multiple activity travel

indicators are estimated jointly. Paper II, III and IV are the first such studies to consider the

endogeneity and indirect effects in weather related travel behaviour studies. The results reveal

significant indirect effects of weather variables on travel behaviour variables. These indirect

effects among activity time use, trip generation, trip chaining and mode share, all reveal that

various activity travel choices are interrelated, and thus the impacts of weather variables on one

travel behaviour variable can have a significant indirect effect on another travel behaviour

variable. The results also reveal that the indirect effects of weather can consist of a large share in

the total effects. Therefore, transport policies targeted for one travel behaviour indicator may not

achieve the desired effect if considerable indirect effects from another travel behaviour indicator

are not considered. For instance, results in Paper II suggest increasing travel demand and higher

cycling share in a “warmer than normal” day in winter. A support infrastructure would be better

in place to service the increased demand and keep the use of more sustainable and greener travel

mode as long as possible. Paper II also highlights the importance of winter road maintenance

especially in urban areas in southern Sweden where heavy snow situation was not as common as

northern Sweden. Among those adverse weather factors, heavy rain, strong wind, bad visibility

and snow, bad visibility is the one which has the most negative impacts on commuters’ activity

participation (activity duration, number of trips and trip chains). For non-commuters, bad

visibility and heavy rain are the two factors with similar impacts which most negatively

influence the activity participation, then followed by the strong wind. Thus, an advanced

preparation especially in foggy days and heavy rain conditions would be required to minimize

the disruption due to the changes in travelers’ daily activity-travel patterns.

By utilizing the subjective weather perception measures (Paper VI), the reference

dependency problem in the effects of weather variables on travel behaviour is addressed. The

results show that the reference point (indifference weather condition) in general corresponds to

the historical mean of the thermal environment. However, individuals with different socio-

demographic profiles have different subjective weather perception under even the same weather

conditions, mainly due to individuals having different perceptions on short term variations of

thermal environment (deviation against its historical mean value). Furthermore, the effect of

subjective weather perception measure also shows heterogeneous effects on leisure activity

participation for individuals with different socio-demographic profiles. These two kinds of

heterogeneity in subjective weather perception can potentially contribute to spatial and temporal

variations of the impacts of weather as discussed above. Understanding how individuals perceive

weather is one step further towards a better understanding of how weather affects individuals’

activity travel decision making process compared to empirical studies that directly link objective

weather measures and travel behaviour variables.

By exploring the role of weather variation on passenger transport CO2 emissions, it is found

that the passenger transport CO2 emissions would be higher in a warmer future climate with

more extreme weather conditions. The emission of a possible scenario ‘mean temperature +4 °C,

Z score + 50%, and precipitation + 20%’ according to IPCC future climate projection is

calculated, which results in 4184.7 g per individual per day (108.1% of the base scenario, the

current weather conditions). This means that CO2 emissions will increase by 8% due to the

changes in weather and climate in a pessimistic future climate scenario. This indicates that if a

large-scale transport demand–supply model does not take into account weather elements, its

prediction of CO2 emissions in the future scenario can be significantly underestimated. Transport

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policies related to climate mitigation should either focus on market penetration of green vehicles

(e.g. electric vehicle) in order to lower emission factors or cope with an increased travel demand,

especially for long-distance car trips in Sweden, to decrease vehicle kilometres travelled.

5. LIMITATIONS AND FUTURE WORK

The time-geography framework can be incorporated to further understand how a local

weather and infrastructure may form a particular micro-meteorological environment which is

perceived by individuals living close to the particular area. Given different urban landscape and

built environment features in different regions, the local thermal environment can be highly

heterogeneous for two regions with different built environment and urban density but under the

same weather conditions. Therefore, future research direction can focus on how local thermal

environment is formed given the weather condition and urban form presented. In terms of

modelling techniques, spatial dependent or locally weighted discrete choice models (Helbichet et

al., 2014) can be applied to capture the combined effect of urban form and weather conditions.

The papers presented in this paper explored various aspects of the impacts of weather on

individual’s observed travel pattern. However, weather may actually act as constraints and affect

individuals’ accessibility and space-time prism (Hägestrand, 1970). For instance, adverse

weather conditions may limit walking and cycling feasibility for a given individual (e.g. certain

cycling lanes may not be functional on snowy days). Inferring the boundary of individual’s

travel in space and time requires observing multiday observation of individual’s travel, while

most existing studies used one-day travel diary. Space-time prism (e.g. Yamamoto et al., 2004)

concept offers a general framework for analysing individual’s ability and constraints travelling

over space and time. Further studies incorporating multiday travel diary can help understanding

how weather plays a role in affecting individuals’ space-time prism.

In paper VI, the subjective weather perception is represented as one 5 point Likert scale

measure. However, actual perception of weather may be multidimensional, e.g. the feeling of

cold/warm, humid/dry, etc. adopting knowledge in biometeorology and psychology may provide

further insights on how subjective weather perception should be measured.

Furthermore, weather may also affect various psychological constructs such as happiness

and well-being, thus influences one’s activity-travel choices. Measuring those psychological

constructs and integrating them into weather related travel behaviour research would provide a

more comprehensive picture of weather impacts. In terms of inference, integrating psychological

constructs in travel behaviour models indicates introducing various latent variables into the

discrete choice modelling framework, which is known as a hybrid choice model (Walkers and

Ben-Akiva, 2002). Given the fact that subjective weather perception measure as a latent variable

itself may also interact with those psychological constructs, it is challenging to get inference

from hybrid choice models with multiple latent variables that are interacted with each other.

The papers presented in this thesis focus on individuals’ travel behaviour. However,

individuals’ daily activities are often interacted with their household members’. Individuals with

different roles in household may negotiate/share household activities. In that sense, weather may

have different roles for different household members and affect the group decision making

mechanism regarding distribution and allocation of household tasks to different household

members (example of household travel choice behaviour: Zhang et al., 2009). Exploring this

interaction given weather conditions forwards the current weather related travel behaviour

research into household oriented travel behaviour analysis. The results would help understand to

what extent weather would affect group decision making regarding activity

distribution/negotiation within a household.

Finally, paper VIII shows that existing knowledge from weather related travel behaviour

research is rarely adopted into cost-benefit analysis that is utilized as the main tool for transport

appraisal, which indicates that the knowledge gained is not used in the practical decision making

process of transport project selections. This is due to the fact that the traditional travel demand-

supply models (four-step model) which serve as a main tool in transport appraisals often do not

consider the demand variation due to changes in weather. Future research should focus on

methodological issues in incorporating weather and climate in large scale travel demand models,

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as well as transport appraisal related issues such as future discounting, effect models, etc.

REFERENCES

Axhausen, K.W., Gärling, T. (1992